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df = read.csv2("D:\\TAM DAN NON-ORTHO\\10. Non ortho_EXAMINATION OF TEETH AND PERIDONTICAL CONDITION\\10.4 MOLAR AND INCISOR HYPOMINERALIZATION\\10.4 MOLAR AND INCISOR HYPOMINERALIZATION.csv")
library(lessR)
## Warning: package 'lessR' was built under R version 4.5.2
## 
## lessR 4.5                            feedback: gerbing@pdx.edu 
## --------------------------------------------------------------
## > d <- Read("")  Read data file, many formats available, e.g., Excel
##   d is the default data frame, data= in analysis routines optional
## 
## Many examples of reading, writing, and manipulating data, graphics,
## testing means and proportions, regression, factor analysis,
## customization, forecasting, and aggregation to pivot tables.
##   Enter: browseVignettes("lessR")
## 
## View lessR updates, now including modern time series forecasting
##   and many, new Plotly interactive visualizations output. Most
##   visualization functions are now reorganized to three functions:
##      Chart(): type="bar", "pie", "radar", "bubble", "treemap", "icicle"
##      X(): type="histogram", "density", "vbs" and more
##      XY(): type="scatter" for a scatterplot, or "contour", "smooth"
##    Most previous function calls still work, such as:
##      BarChart(), Histogram, and Plot().
##   Enter: news(package="lessR"), or ?Chart, ?X, or ?XY
## There is also Flows() for Sankey flow diagrams, see ?Flows
## 
## Interactive data analysis for constructing visualizations.
##   Enter: interact()
library(labelled)
## Warning: package 'labelled' was built under R version 4.5.3
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:lessR':
## 
##     order_by, recode, rename
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(writexl)
## Warning: package 'writexl' was built under R version 4.5.3
# 1. MÃ HÓA KHIẾM KHUYẾT MEN RĂNG VÀ GHI ĐÈ LÊN df
df <- df %>%
  mutate(
    # Mã hóa cho tất cả các cột trừ No và ID
    across(
      -c(No, ID),
      ~ factor(., 
               levels = c(0, 1, 11, 12, 13, 14, 2, 21, 22, 4, 5, 6, 7, 
                          121, 221, 321, 122, 222, 322, 888, 999), 
               labels = c("No visible enamel defect", 
                          "Enamel defect, not MIH/HSPM", 
                          "Diffuse opacities", 
                          "Hypoplasia", 
                          "Amelogenesis imperfecta", 
                          "Hypomineralisation defect (not MIH/HSPM)", 
                          "Demarcated opacities", 
                          "White or creamy demarcated opacities", 
                          "Yellow or brown demarcated opacities", 
                          "Atypical restoration", 
                          "Atypical caries", 
                          "Missing due to MIH/HSPM", 
                          "Cannot be scored", 
                          "White or creamy demarcated opacities and Less than 1/3 of the tooth affected", 
                          "White or creamy demarcated opacities and At least 1/3 but less than 2/3 of the tooth affected", 
                          "White or creamy demarcated opacities and At least 2/3 of the tooth affected", 
                          "Yellow or brown demarcated opacities and Less than 1/3 of the tooth affected", 
                          "Yellow or brown demarcated opacities and At least 1/3 but less than 2/3 of the tooth affected", 
                          "Yellow or brown demarcated opacities and At least 2/3 of the tooth affected", 
                          "Not recorded", 
                          "Missing tooth"))
    )
  )

# 2. GẮN NHÃN MÔ TẢ (LABELS) HOÀN TOÀN TỰ ĐỘNG
# Bước 2.1: Lấy danh sách các biến răng (trừ No và ID)
enamel_vars <- setdiff(names(df), c("No", "ID"))

# Bước 2.2: Tự động thay thế các hậu tố viết tắt thành tên mặt răng đầy đủ
nhan_enamel <- enamel_vars %>%
  gsub("_Bu", " Buccal Surface", .) %>%
  gsub("_Oc", " Occlusal Surface", .) %>%
  gsub("_Li", " Lingual Surface", .) %>%
  paste("Tooth", .) # Thêm chữ "Tooth" lên đầu mỗi nhãn

# Bước 2.3: Gán danh sách nhãn vào bảng df
names(nhan_enamel) <- enamel_vars
var_label(df) <- as.list(nhan_enamel)
# Tạo một bảng copy tạm thời để đổi tên tiêu đề
df_export <- df %>%
  # Lệnh này biến toàn bộ các "Nhãn dài" thành tên cột thực sự
  setNames(var_label(., unlist = TRUE))

# Sau đó xuất cái bảng tạm này ra Excel
write_xlsx(df_export, "D:\\TAM DAN - NON ORTHO (NEW)\\10.4\\10.4 MOLAR AND INCISOR HYPOMINERALIZATION.xlsx")